A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.

Automated summary

This paper explores the various applications of behavior modeling in the intelligent environment domain. It proposes the use of embeddings to represent user actions for behavior prediction and compares different approaches. The study tests multiple model architectures and evaluates embedding retrofitting methods to determine the best approach for behavior modeling.